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09/14/2023
Nonlinear model order reduction for problems with microstructure using mesh informed neural networks
Many applications in computational physics involve approximating problem...
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07/17/2023
Robust Preconditioning of mixed-dimensional PDEs on 3d-1d domains coupled with Lagrange multipliers
In the context of micro-circulation, the coexistence of two distinct len...
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03/19/2023
Reduced Lagrange multiplier approach for non-matching coupling of mixed-dimensional domains
Many physical problems involving heterogeneous spatial scales, such as t...
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02/16/2023
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
Uncertainty quantification (UQ) tasks, such as sensitivity analysis and ...
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07/04/2022
Approximation bounds for convolutional neural networks in operator learning
Recently, deep Convolutional Neural Networks (CNNs) have proven to be su...
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03/22/2022
Learning Operators with Mesh-Informed Neural Networks
Thanks to their universal approximation properties and new efficient tra...
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03/10/2021
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations
Within the framework of parameter dependent PDEs, we develop a construct...
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02/23/2021
Learning High-Order Interactions via Targeted Pattern Search
Logistic Regression (LR) is a widely used statistical method in empirica...
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11/26/2020
Iterative splitting schemes for a soft material poromechanics model
We address numerical solvers for a poromechanics model particularly adap...
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04/06/2020